Multi‐Diseases Detection with Memristive System on Chip Article Swipe
Zihan Wang
,
Daniel W. Yang
,
Zerui Liu
,
Eryun Yan
,
Heming Sun
,
Ning Ge
,
Miao Hu
,
Wei Wu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/aisy.202400736
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/aisy.202400736
This study presents the first implementation of multilayer neural networks on a memristor/complementary metal‐oxide‐semiconductor (CMOS)‐integrated system‐on‐chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative artificial intelligence techniques are used to enhance the dataset, improving the classifier's robustness and diversity. The system achieves notable performance with low latency, high accuracy (91.82%), and energy efficiency, facilitated by end‐to‐end execution on a memristor‐based SoC with ten 256 × 256 crossbar arrays and an integrated on‐chip processor. This research showcases the transformative potential of memristive in‐memory computing hardware in accelerating machine learning applications for medical diagnostics.
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- article
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- https://doi.org/10.1002/aisy.202400736
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https://doi.org/10.1002/aisy.202400736Digital Object Identifier
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Multi‐Diseases Detection with Memristive System on ChipWork title
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articleOpenAlex work type
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2025Year of publication
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2025-02-11Full publication date if available
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Zihan Wang, Daniel W. Yang, Zerui Liu, Eryun Yan, Heming Sun, Ning Ge, Miao Hu, Wei WuList of authors in order
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https://doi.org/10.1002/aisy.202400736Publisher landing page
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goldOpen access status per OpenAlex
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Chip, Computer science, Nanotechnology, Embedded system, Materials science, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 17, 34 |
| abstract_inverted_index.× | 69 |
| abstract_inverted_index.256 | 68, 70 |
| abstract_inverted_index.SoC | 65 |
| abstract_inverted_index.The | 44 |
| abstract_inverted_index.and | 42, 55, 73 |
| abstract_inverted_index.are | 32 |
| abstract_inverted_index.for | 94 |
| abstract_inverted_index.low | 50 |
| abstract_inverted_index.ten | 67 |
| abstract_inverted_index.the | 3, 36, 39, 81 |
| abstract_inverted_index.This | 0, 78 |
| abstract_inverted_index.high | 52 |
| abstract_inverted_index.used | 33 |
| abstract_inverted_index.with | 49, 66 |
| abstract_inverted_index.(SoC) | 16 |
| abstract_inverted_index.data, | 27 |
| abstract_inverted_index.first | 4 |
| abstract_inverted_index.study | 1 |
| abstract_inverted_index.arrays | 72 |
| abstract_inverted_index.detect | 19 |
| abstract_inverted_index.energy | 56 |
| abstract_inverted_index.neural | 8 |
| abstract_inverted_index.system | 45 |
| abstract_inverted_index.enhance | 35 |
| abstract_inverted_index.machine | 91 |
| abstract_inverted_index.medical | 26, 95 |
| abstract_inverted_index.notable | 47 |
| abstract_inverted_index.accuracy | 53 |
| abstract_inverted_index.achieves | 46 |
| abstract_inverted_index.crossbar | 71 |
| abstract_inverted_index.dataset, | 37 |
| abstract_inverted_index.hardware | 88 |
| abstract_inverted_index.latency, | 51 |
| abstract_inverted_index.learning | 92 |
| abstract_inverted_index.multiple | 20 |
| abstract_inverted_index.networks | 9 |
| abstract_inverted_index.overcome | 23 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.research | 79 |
| abstract_inverted_index.(91.82%), | 54 |
| abstract_inverted_index.computing | 87 |
| abstract_inverted_index.diseases. | 21 |
| abstract_inverted_index.execution | 61 |
| abstract_inverted_index.improving | 38 |
| abstract_inverted_index.on‐chip | 76 |
| abstract_inverted_index.potential | 83 |
| abstract_inverted_index.showcases | 80 |
| abstract_inverted_index.artificial | 29 |
| abstract_inverted_index.diversity. | 43 |
| abstract_inverted_index.generative | 28 |
| abstract_inverted_index.integrated | 75 |
| abstract_inverted_index.memristive | 85 |
| abstract_inverted_index.multilayer | 7 |
| abstract_inverted_index.processor. | 77 |
| abstract_inverted_index.robustness | 41 |
| abstract_inverted_index.techniques | 31 |
| abstract_inverted_index.efficiency, | 57 |
| abstract_inverted_index.facilitated | 58 |
| abstract_inverted_index.in‐memory | 86 |
| abstract_inverted_index.limitations | 24 |
| abstract_inverted_index.performance | 48 |
| abstract_inverted_index.accelerating | 90 |
| abstract_inverted_index.applications | 93 |
| abstract_inverted_index.classifier's | 40 |
| abstract_inverted_index.diagnostics. | 96 |
| abstract_inverted_index.intelligence | 30 |
| abstract_inverted_index.end‐to‐end | 60 |
| abstract_inverted_index.implementation | 5 |
| abstract_inverted_index.simultaneously | 18 |
| abstract_inverted_index.transformative | 82 |
| abstract_inverted_index.memristor‐based | 64 |
| abstract_inverted_index.system‐on‐chip | 15 |
| abstract_inverted_index.(CMOS)‐integrated | 14 |
| abstract_inverted_index.memristor/complementary | 12 |
| abstract_inverted_index.metal‐oxide‐semiconductor | 13 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.86410179 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |